Dina Machuve
Impact in
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- Online Learning and Analytics
- Animal Science and Zoology top 10%
- Livestock and Poultry Management
Papers in
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- Data Stream Mining Techniques 3
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- ICT in Developing Communities 4
- Co-authors
- Neema Mduma (8 shared papers)Khamisi Kalegele (4 shared papers)Sawahiko Shimada (1 shared paper)Baraka Maiseli (1 shared paper)Karen Bradshaw (1 shared paper)Thomas Clemen (1 shared paper)Michael Kisangiri (4 shared papers)Anael Sam (3 shared papers)
In The Last Decade
Dina Machuve
33 papers receiving 429 citations
Peers
Comparison fields: 5 of 87
- Computer Science Applications 89
- Animal Science and Zoology 66
- Health Information Management 26
- Analytical Chemistry 52
- Health Informatics 7
Countries citing papers authored by Dina Machuve
This map shows the geographic impact of Dina Machuve's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dina Machuve with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dina Machuve more than expected).
Fields of papers citing papers by Dina Machuve
This network shows the impact of papers produced by Dina Machuve. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dina Machuve. The network helps show where Dina Machuve may publish in the future.
Co-authors
The 18 scholars most cited alongside Dina Machuve, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 80 | |
| 2 | 2020 | 69 | |
| 3 | 2020 | 61 | |
| 4 | 2022 | 54 | |
| 5 | 2021 | 44 | |
| 6 | 2019 | 29 | |
| 7 | 2021 | 25 | |
| 8 | 2021 | 20 | |
| 9 | 2021 | 13 | |
| 10 | Overview Applications of Data Mining In Health Care: The Case Study of Arusha Region | 2013 | 11 |
| 11 | 2019 | 9 | |
| 12 | Deign of Low Cost Blood Pressure and Body Temperature interface | 2013 | 6 |
| 13 | 2021 | 5 | |
| 14 | 2020 | 4 | |
| 15 | 2024 | 4 | |
| 16 | 2019 | 4 | |
| 17 | CONCEPTUAL MODEL OF INFORMATION LOGISTICS IN VALUE CHAIN ANALYSIS OF FOOD PROCESSING SMES IN TANZANIA | 2015 | 3 |
| 18 | 2014 | 3 | |
| 19 | 2018 | 3 | |
| 20 | 2013 | 2 |
About Dina Machuve
Dina Machuve is a scholar working on Artificial Intelligence, Information Systems, Plant Science, Computer Science Applications and Computer Networks and Communications, having authored 44 papers that have together received 468 indexed citations. Recurring topics across this work include Smart Agriculture and AI (6 papers), ICT in Developing Communities (4 papers), Online Learning and Analytics (4 papers), Leaf Properties and Growth Measurement (3 papers), Livestock and Poultry Management (3 papers), Data Stream Mining Techniques (3 papers), Global Maternal and Child Health (2 papers) and Banana Cultivation and Research (2 papers). The work is most often cited by research in Computer Science Applications (89 citations), Animal Science and Zoology (66 citations), Health Information Management (26 citations), Analytical Chemistry (52 citations) and Health Informatics (7 citations). Dina Machuve has collaborated with scholars based in Tanzania, Finland and Germany. Frequent co-authors include Neema Mduma, Khamisi Kalegele, Sawahiko Shimada, Baraka Maiseli, Karen Bradshaw, Thomas Clemen, Michael Kisangiri, Anael Sam, Pirkko Nykänen and Mussa Ally Dida. Their work appears in journals such as Data in Brief, Scientific African, Data Science Journal, Applied Artificial Intelligence and Frontiers in Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.